IM in topologically unknown networks. More recently, researchers have paid attention to a more challenging IM problem where the structure of the underlying network is initially unknown. Following the concept of active learning for classification [1]}, [2]}, dynamic IM over a series of rounds in which information on edges is collected after each round has been studied [3]}, [4]}. Exploratory IM (EIM) was introduced along with a solution that queries individual nodes to learn their edges by assuming that the underlying network is built upon a stochastic block model [5]}. As follow-up studies on EIM, CHANGE [6]} and Geometric-DQN [7]} were presented using the friendship paradox and patterns learned from a set of similar graphs, respectively. A theoretical analysis on the performance of IM was studied in settings where a subgraph is retrieved via random node sampling [8]}, [9]}. Another attempt to solve the IM problem in unknown networks involves learning an influence function based on observed cascades [10]}.
